# Trustworthy AI in the Age of Pervasive Computing and Big Data

arXiv: https://arxiv.org/abs/2002.05657 · HTML: https://mlsanity.com/paper/2002.05657/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

## Built on by (most cited first, 9 of 9)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey](https://mlsanity.com/paper/2412.13840/) (2024)
- [Personalized Federated Deep Learning for Pain Estimation From Face Images](https://mlsanity.com/paper/2101.04800/) (2021)
- [Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach](https://mlsanity.com/paper/2410.21346/) (2024)
- [A Seven-Layer Model for Standardising AI Fairness Assessment](https://mlsanity.com/paper/2212.11207/) (2022)
- [Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review](https://mlsanity.com/paper/2308.09380/) (2023)
- [Trustworthy XAI and Application](https://mlsanity.com/paper/2410.17139/) (2024)
- [Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability](https://mlsanity.com/paper/2608.02238/) (2026)

## Abstract (arXiv)

The era of pervasive computing has resulted in countless devices that continuously monitor users and their environment, generating an abundance of user behavioural data. Such data may support improving the quality of service, but may also lead to adverse usages such as surveillance and advertisement. In parallel, Artificial Intelligence (AI) systems are being applied to sensitive fields such as healthcare, justice, or human resources, raising multiple concerns on the trustworthiness of such systems. Trust in AI systems is thus intrinsically linked to ethics, including the ethics of algorithms, the ethics of data, or the ethics of practice. In this paper, we formalise the requirements of trustworthy AI systems through an ethics perspective. We specifically focus on the aspects that can be integrated into the design and development of AI systems. After discussing the state of research and the remaining challenges, we show how a concrete use-case in smart cities can benefit from these methods.